Lilly Tech Systems
Data Science Interview Prep
Prepare for data science interviews at top tech companies with 64+ real questions and detailed model answers. From statistics and probability to SQL, pandas, A/B testing, and full case studies — everything you need to land your data science role.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. DS Interview Overview
Understand interview types at top companies, what each round tests, and how to build an effective preparation strategy.
2. Statistics Questions
15 Q&A on distributions, central limit theorem, p-values, confidence intervals, hypothesis testing, and Bayesian vs frequentist approaches.
3. Probability Questions
12 Q&A on conditional probability, Bayes theorem, combinatorics, expected value, and common probability puzzles.
4. A/B Testing Questions
12 Q&A on experiment design, sample size calculation, statistical significance, multi-arm bandits, and common pitfalls.
5. SQL for Data Science
10 SQL questions with solutions covering window functions, CTEs, self-joins, aggregations, and query optimization.
6. Pandas & Python Coding
10 hands-on challenges: data manipulation, groupby, merge, pivot tables, time series, and visualization.
7. Case Study Questions
5 full case studies: metric design, root cause analysis, product analytics, recommendation impact, and churn prediction.
8. Practice Questions & Tips
Rapid fire questions, presentation tips for data science interviews, and a comprehensive FAQ accordion.
What You'll Learn
By the end of this course, you'll be able to:
Ace Statistics & Probability
Confidently answer questions on distributions, hypothesis testing, Bayes theorem, and expected value under interview pressure.
Write Production SQL
Solve complex SQL problems using window functions, CTEs, self-joins, and optimize queries for large datasets.
Design Experiments
Explain A/B testing methodology, calculate sample sizes, identify pitfalls, and discuss multi-arm bandit strategies.
Solve Case Studies
Structure your approach to open-ended product analytics problems and present data-driven recommendations clearly.